{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding an alpha to Prediction Error plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import yellowbrick as yb\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  import sys\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "concrete = pd.read_csv('data/concrete/concrete.csv')\n",
    "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n",
    "target_name = 'strength'\n",
    "\n",
    "# Get the X and y data from the DataFrame\n",
    "X = concrete[feature_names].as_matrix()\n",
    "y = concrete[target_name].as_matrix()\n",
    "\n",
    "# Create the train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  linalg.lstsq(X, y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "from yellowbrick.regressor import PredictionError\n",
    "\n",
    "# Instantiate the linear model and visualizer\n",
    "lasso = Lasso()\n",
    "visualizer = PredictionError(lasso)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does alpha work now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = PredictionError(lasso, alpha=0.1)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yellowbrick.draw\n",
    "# Utilities for common matplotlib drawing procedures.\n",
    "#\n",
    "# Author:  Benjamin Bengfort <benjamin@bengfort.com>\n",
    "# Created: Sun Aug 19 10:35:50 2018 -0400\n",
    "#\n",
    "# ID: draw.py [] benjamin@bengfort.com $\n",
    "\n",
    "\"\"\"\n",
    "Utilities for common matplotlib drawing procedures.\n",
    "\"\"\"\n",
    "\n",
    "##########################################################################\n",
    "## Imports\n",
    "##########################################################################\n",
    "\n",
    "from yellowbrick.base import Visualizer\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "\n",
    "from matplotlib import patches\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "## Legend Drawing Utilities\n",
    "##########################################################################\n",
    "\n",
    "def manual_legend(g, labels, colors, **legend_kwargs):\n",
    "    \"\"\"\n",
    "    Adds a manual legend for a scatter plot to the visualizer where the labels\n",
    "    and associated colors are drawn with circle patches instead of determining\n",
    "    them from the labels of the artist objects on the axes. This helper is\n",
    "    used either when there are a lot of duplicate labels, no labeled artists,\n",
    "    or when the color of the legend doesn't exactly match the color in the\n",
    "    figure (e.g. because of the use of transparency).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    g : Visualizer or Axes object\n",
    "        The graph to draw the legend on, either a Visualizer or a matplotlib\n",
    "        Axes object. If None, the current axes are drawn on, but this is not\n",
    "        recommended.\n",
    "\n",
    "    labels : list of str\n",
    "        The text labels to associate with the legend. Note that the labels\n",
    "        will be added to the legend in the order specified.\n",
    "\n",
    "    colors : list of colors\n",
    "        A list of any valid matplotlib color reference. The number of colors\n",
    "        specified must be equal to the number of labels.\n",
    "\n",
    "    legend_kwargs : dict\n",
    "        Any additional keyword arguments to pass to the legend.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    legend: Legend artist\n",
    "        The artist created by the ax.legend() call, returned for further\n",
    "        manipulation if required by the caller.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Right now this method simply draws the patches as rectangles and cannot\n",
    "    take into account the line or scatter plot properties (e.g. line style or\n",
    "    marker style). It is possible to add Line2D patches to the artist that do\n",
    "    add manual styles like this, which we can explore in the future.\n",
    "\n",
    "    .. seealso:: https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html\n",
    "    \"\"\"\n",
    "    # Get access to the matplotlib Axes\n",
    "    if isinstance(g, Visualizer):\n",
    "        g = g.ax\n",
    "    elif g is None:\n",
    "        g = plt.gca()\n",
    "\n",
    "    # Ensure that labels and colors are the same length to prevent odd behavior.\n",
    "    if len(colors) != len(labels):\n",
    "        raise YellowbrickValueError(\n",
    "            \"please specify the same number of colors as labels!\"\n",
    "        )\n",
    "\n",
    "    # Create the legend handles with the associated colors and labels\n",
    "    handles = [\n",
    "        patches.Patch(color=color, label=label)\n",
    "        for color, label in zip(colors, labels)\n",
    "    ]\n",
    "\n",
    "    # Return the Legend artist\n",
    "    return g.legend(handles=handles, **legend_kwargs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now with alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    # Only available in Matplotlib >= 2.0.2\n",
    "    from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "except ImportError:\n",
    "    make_axes_locatable = None\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from yellowbrick.regressor.base import RegressionScoreVisualizer\n",
    "from yellowbrick.style.palettes import LINE_COLOR\n",
    "from yellowbrick.utils.decorators import memoized\n",
    "from yellowbrick.exceptions import YellowbrickValueError\n",
    "from yellowbrick.bestfit import draw_best_fit, draw_identity_line\n",
    "\n",
    "\n",
    "class PredictionError(RegressionScoreVisualizer):\n",
    "    \"\"\"\n",
    "    The prediction error visualizer plots the actual targets from the dataset\n",
    "    against the predicted values generated by our model(s). This visualizer is\n",
    "    used to dectect noise or heteroscedasticity along a range of the target\n",
    "    domain.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    model : a Scikit-Learn regressor\n",
    "        Should be an instance of a regressor, otherwise will raise a\n",
    "        YellowbrickTypeError exception on instantiation.\n",
    "\n",
    "    ax : matplotlib Axes, default: None\n",
    "        The axes to plot the figure on. If None is passed in the current axes\n",
    "        will be used (or generated if required).\n",
    "\n",
    "    shared_limits : bool, default: True\n",
    "        If shared_limits is True, the range of the X and Y axis limits will\n",
    "        be identical, creating a square graphic with a true 45 degree line.\n",
    "        In this form, it is easier to diagnose under- or over- prediction,\n",
    "        though the figure will become more sparse. To localize points, set\n",
    "        shared_limits to False, but note that this will distort the figure\n",
    "        and should be accounted for during analysis.\n",
    "\n",
    "    bestfit : bool, default: True\n",
    "        Draw a linear best fit line to estimate the correlation between the\n",
    "        predicted and measured value of the target variable. The color of\n",
    "        the bestfit line is determined by the ``line_color`` argument.\n",
    "\n",
    "    identity: bool, default: True\n",
    "        Draw the 45 degree identity line, y=x in order to better show the\n",
    "        relationship or pattern of the residuals. E.g. to estimate if the\n",
    "        model is over- or under- estimating the given values. The color of the\n",
    "        identity line is a muted version of the ``line_color`` argument.\n",
    "\n",
    "    point_color : color\n",
    "        Defines the color of the error points; can be any matplotlib color.\n",
    "\n",
    "    line_color : color\n",
    "        Defines the color of the best fit line; can be any matplotlib color.\n",
    "\n",
    "    alpha : float, default: 1.0\n",
    "        Specify a transparency where 1 is completely opaque and 0 is completely\n",
    "        transparent. This property makes densely clustered points more visible.\n",
    "\n",
    "    kwargs : dict\n",
    "        Keyword arguments that are passed to the base class and may influence\n",
    "        the visualization as defined in other Visualizers.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "\n",
    "    >>> from yellowbrick.regressor import PredictionError\n",
    "    >>> from sklearn.linear_model import Lasso\n",
    "    >>> model = PredictionError(Lasso())\n",
    "    >>> model.fit(X_train, y_train)\n",
    "    >>> model.score(X_test, y_test)\n",
    "    >>> model.show()\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "\n",
    "    PredictionError is a ScoreVisualizer, meaning that it wraps a model and\n",
    "    its primary entry point is the `score()` method.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model, ax=None, shared_limits=True,\n",
    "                 bestfit=True, identity=True, alpha=1.0, **kwargs):\n",
    "        # Initialize the visualizer\n",
    "        super(PredictionError, self).__init__(model, ax=ax, **kwargs)\n",
    "\n",
    "        # Visual arguments\n",
    "        self.colors = {\n",
    "            'point': kwargs.pop('point_color', None),\n",
    "            'line': kwargs.pop('line_color', LINE_COLOR),\n",
    "        }\n",
    "        \n",
    "        if self.colors['point'] == None:\n",
    "            self.colors['point'] = 'b'\n",
    "            \n",
    "        # Drawing arguments\n",
    "        self.shared_limits = shared_limits\n",
    "        self.bestfit = bestfit\n",
    "        self.identity = identity\n",
    "        self.alpha = alpha\n",
    "\n",
    "    def score(self, X, y=None, **kwargs):\n",
    "        \"\"\"\n",
    "        The score function is the hook for visual interaction. Pass in test\n",
    "        data and the visualizer will create predictions on the data and\n",
    "        evaluate them with respect to the test values. The evaluation will\n",
    "        then be passed to draw() and the result of the estimator score will\n",
    "        be returned.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like\n",
    "            X (also X_test) are the dependent variables of test set to predict\n",
    "\n",
    "        y : array-like\n",
    "            y (also y_test) is the independent actual variables to score against\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        score : float\n",
    "        \"\"\"\n",
    "        self.score_ =  self.estimator.score(X, y, **kwargs)\n",
    "\n",
    "        y_pred = self.predict(X)\n",
    "        self.draw(y, y_pred)\n",
    "\n",
    "        return self.score_\n",
    "\n",
    "    def draw(self, y, y_pred):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        y : ndarray or Series of length n\n",
    "            An array or series of target or class values\n",
    "\n",
    "        y_pred : ndarray or Series of length n\n",
    "            An array or series of predicted target values\n",
    "\n",
    "        Returns\n",
    "        ------\n",
    "        ax : the axis with the plotted figure\n",
    "        \"\"\"\n",
    "        self.ax.scatter(\n",
    "            y,\n",
    "            y_pred,\n",
    "            c=self.colors['point'],\n",
    "            alpha=self.alpha)\n",
    "\n",
    "        # Set the axes limits based on the range of X and Y data\n",
    "        # NOTE: shared_limits will be accounted for in finalize()\n",
    "        # TODO: do better than add one for really small residuals\n",
    "        self.ax.set_xlim(y.min()-1, y.max()+1)\n",
    "        self.ax.set_ylim(y_pred.min()-1, y_pred.max()+1)\n",
    "\n",
    "        return self.ax\n",
    "\n",
    "    def finalize(self, **kwargs):\n",
    "        \"\"\"\n",
    "        Finalize executes any subclass-specific axes finalization steps.\n",
    "        The user calls show and show calls finalize.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        kwargs: generic keyword arguments.\n",
    "        \"\"\"\n",
    "        # Set the title on the plot\n",
    "        self.set_title(\n",
    "            'Prediction Error for {}'.format(self.name)\n",
    "        )\n",
    "\n",
    "        # Square the axes to ensure a 45 degree line\n",
    "        if self.shared_limits:\n",
    "            # Get the current limits\n",
    "            ylim = self.ax.get_ylim()\n",
    "            xlim = self.ax.get_xlim()\n",
    "\n",
    "            # Find the range that captures all data\n",
    "            bounds = (\n",
    "                min(ylim[0], xlim[0]),\n",
    "                max(ylim[1], xlim[1]),\n",
    "            )\n",
    "\n",
    "            # Reset the limits\n",
    "            self.ax.set_xlim(bounds)\n",
    "            self.ax.set_ylim(bounds)\n",
    "\n",
    "            # Ensure the aspect ratio is square\n",
    "            self.ax.set_aspect('equal', adjustable='box')\n",
    "\n",
    "        # Set the legend with full opacity patches using manual legend\n",
    "        label = [\"$R^2 = {:0.3f}$\".format(self.score_)]\n",
    "        manual_legend(\n",
    "            self, label, self.colors['point'], loc='best', frameon=True\n",
    "        )\n",
    "\n",
    "        # TODO If score is happening inside a loop, draw would get called multiple times.\n",
    "        # Ideally we'd want the best fit line to be drawn only once\n",
    "#         if self.bestfit:\n",
    "#             draw_best_fit(\n",
    "#                 y, y_pred, self.ax, 'linear', ls='--', lw=2,\n",
    "#                 c=self.colors['line'], label='best fit'\n",
    "#             )\n",
    "            \n",
    "        # Draw the 45 degree line\n",
    "        if self.identity:\n",
    "            draw_identity_line(\n",
    "                ax=self.ax, ls='--', lw=2, c=self.colors['line'],\n",
    "                alpha=0.5, label=\"identity\"\n",
    "            )\n",
    "\n",
    "        # Set the axes labels\n",
    "        self.ax.set_ylabel(r'$\\hat{y}$')\n",
    "        self.ax.set_xlabel(r'$y$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = PredictionError(lasso, alpha=0.5)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualizer = PredictionError(lasso, alpha=0.475)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualizer = PredictionError(lasso, alpha=0.9)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualizer = PredictionError(lasso, alpha=0.3)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How about Residuals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from yellowbrick.regressor import ResidualsPlot\n",
    "\n",
    "# Instantiate the linear model and visualizer\n",
    "ridge = Ridge()\n",
    "visualizer = ResidualsPlot(ridge)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the model\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.show()                 # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()\n",
    "visualizer = ResidualsPlot(ridge, alpha=0.3)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the model\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.show()                 # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################\n",
    "## Residuals Plots\n",
    "##########################################################################\n",
    "\n",
    "class ResidualsPlot(RegressionScoreVisualizer):\n",
    "    \"\"\"\n",
    "    A residual plot shows the residuals on the vertical axis and the\n",
    "    independent variable on the horizontal axis.\n",
    "\n",
    "    If the points are randomly dispersed around the horizontal axis, a linear\n",
    "    regression model is appropriate for the data; otherwise, a non-linear\n",
    "    model is more appropriate.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : a Scikit-Learn regressor\n",
    "        Should be an instance of a regressor, otherwise will raise a\n",
    "        YellowbrickTypeError exception on instantiation.\n",
    "\n",
    "    ax : matplotlib Axes, default: None\n",
    "        The axes to plot the figure on. If None is passed in the current axes\n",
    "        will be used (or generated if required).\n",
    "\n",
    "    hist : {True, False, None, 'density', 'frequency'}, default: True\n",
    "        Draw a histogram showing the distribution of the residuals on the\n",
    "        right side of the figure. Requires Matplotlib >= 2.0.2.\n",
    "        If set to 'density', the probability density function will be plotted.\n",
    "        If set to True or 'frequency' then the frequency will be plotted.\n",
    "\n",
    "    train_color : color, default: 'b'\n",
    "        Residuals for training data are ploted with this color but also\n",
    "        given an opacity of 0.5 to ensure that the test data residuals\n",
    "        are more visible. Can be any matplotlib color.\n",
    "\n",
    "    test_color : color, default: 'g'\n",
    "        Residuals for test data are plotted with this color. In order to\n",
    "        create generalizable models, reserved test data residuals are of\n",
    "        the most analytical interest, so these points are highlighted by\n",
    "        having full opacity. Can be any matplotlib color.\n",
    "\n",
    "    line_color : color, default: dark grey\n",
    "        Defines the color of the zero error line, can be any matplotlib color.\n",
    "\n",
    "    alpha : float, default: 1.0\n",
    "        Specify a transparency where 1 is completely opaque and 0 is completely\n",
    "        transparent. This property makes densely clustered points more visible.\n",
    "\n",
    "    kwargs : dict\n",
    "        Keyword arguments that are passed to the base class and may influence\n",
    "        the visualization as defined in other Visualizers.\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "\n",
    "    >>> from yellowbrick.regressor import ResidualsPlot\n",
    "    >>> from sklearn.linear_model import Ridge\n",
    "    >>> model = ResidualsPlot(Ridge())\n",
    "    >>> model.fit(X_train, y_train)\n",
    "    >>> model.score(X_test, y_test)\n",
    "    >>> model.show()\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    ResidualsPlot is a ScoreVisualizer, meaning that it wraps a model and\n",
    "    its primary entry point is the ``score()`` method.\n",
    "\n",
    "    The residuals histogram feature requires matplotlib 2.0.2 or greater.\n",
    "    \"\"\"\n",
    "    def __init__(self, model, ax=None, hist=True, train_color='b',\n",
    "                 test_color='g', line_color=LINE_COLOR, alpha=1.0,\n",
    "                 **kwargs):\n",
    "\n",
    "        super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs)\n",
    "\n",
    "        # TODO: allow more scatter plot arguments for train and test points\n",
    "        # See #475 (RE: ScatterPlotMixin)\n",
    "        self.colors = {\n",
    "            'train_point': train_color,\n",
    "            'test_point': test_color,\n",
    "            'line': line_color,\n",
    "        }\n",
    "\n",
    "        self.hist = hist\n",
    "        if self.hist not in {True, 'density', 'frequency', None, False}:\n",
    "                raise YellowbrickValueError(\n",
    "                    \"'{}' is an invalid argument for hist, use None, True, \" \\\n",
    "                    \"False, 'density', or 'frequency'\".format(hist)\n",
    "                )\n",
    "\n",
    "        if self.hist in {True, 'density', 'frequency'}:\n",
    "            self.hax # If hist is True, test the version availability\n",
    "\n",
    "        # Store labels and colors for the legend ordered by call\n",
    "        self._labels, self._colors = [], []\n",
    "\n",
    "        self.alpha = alpha\n",
    "\n",
    "    @memoized\n",
    "    def hax(self):\n",
    "        \"\"\"\n",
    "        Returns the histogram axes, creating it only on demand.\n",
    "        \"\"\"\n",
    "        if make_axes_locatable is None:\n",
    "            raise YellowbrickValueError((\n",
    "                \"residuals histogram requires matplotlib 2.0.2 or greater \"\n",
    "                \"please upgrade matplotlib or set hist=False on the visualizer\"\n",
    "            ))\n",
    "\n",
    "        divider = make_axes_locatable(self.ax)\n",
    "\n",
    "        hax = divider.append_axes(\"right\", size=1, pad=0.1, sharey=self.ax)\n",
    "        hax.yaxis.tick_right()\n",
    "        hax.grid(False, axis='x')\n",
    "\n",
    "        return hax\n",
    "\n",
    "    def fit(self, X, y, **kwargs):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : ndarray or DataFrame of shape n x m\n",
    "            A matrix of n instances with m features\n",
    "\n",
    "        y : ndarray or Series of length n\n",
    "            An array or series of target values\n",
    "\n",
    "        kwargs: keyword arguments passed to Scikit-Learn API.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : visualizer instance\n",
    "        \"\"\"\n",
    "        super(ResidualsPlot, self).fit(X, y, **kwargs)\n",
    "        self.score(X, y, train=True)\n",
    "        return self\n",
    "\n",
    "    def score(self, X, y=None, train=False, **kwargs):\n",
    "        \"\"\"\n",
    "        Generates predicted target values using the Scikit-Learn\n",
    "        estimator.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like\n",
    "            X (also X_test) are the dependent variables of test set to predict\n",
    "\n",
    "        y : array-like\n",
    "            y (also y_test) is the independent actual variables to score against\n",
    "\n",
    "        train : boolean\n",
    "            If False, `score` assumes that the residual points being plotted\n",
    "            are from the test data; if True, `score` assumes the residuals\n",
    "            are the train data.\n",
    "\n",
    "        Returns\n",
    "        ------\n",
    "        score : float\n",
    "            The score of the underlying estimator, usually the R-squared score\n",
    "            for regression estimators.\n",
    "        \"\"\"\n",
    "        score = self.estimator.score(X, y, **kwargs)\n",
    "        if train:\n",
    "            self.train_score_ = score\n",
    "        else:\n",
    "            self.test_score_ = score\n",
    "\n",
    "        y_pred = self.predict(X)\n",
    "        scores = y_pred - y\n",
    "        self.draw(y_pred, scores, train=train)\n",
    "\n",
    "        return score\n",
    "\n",
    "    def draw(self, y_pred, residuals, train=False, **kwargs):\n",
    "        \"\"\"\n",
    "        Draw the residuals against the predicted value for the specified split.\n",
    "        It is best to draw the training split first, then the test split so\n",
    "        that the test split (usually smaller) is above the training split;\n",
    "        particularly if the histogram is turned on.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        y_pred : ndarray or Series of length n\n",
    "            An array or series of predicted target values\n",
    "\n",
    "        residuals : ndarray or Series of length n\n",
    "            An array or series of the difference between the predicted and the\n",
    "            target values\n",
    "\n",
    "        train : boolean, default: False\n",
    "            If False, `draw` assumes that the residual points being plotted\n",
    "            are from the test data; if True, `draw` assumes the residuals\n",
    "            are the train data.\n",
    "\n",
    "        Returns\n",
    "        ------\n",
    "        ax : the axis with the plotted figure\n",
    "        \"\"\"\n",
    "\n",
    "        if train:\n",
    "            color = self.colors['train_point']\n",
    "            label = \"Train $R^2 = {:0.3f}$\".format(self.train_score_)\n",
    "        else:\n",
    "            color = self.colors['test_point']\n",
    "            label = \"Test $R^2 = {:0.3f}$\".format(self.test_score_)\n",
    "\n",
    "        # Update the legend information\n",
    "        self._labels.append(label)\n",
    "        self._colors.append(color)\n",
    "\n",
    "        # Draw the residuals scatter plot\n",
    "        self.ax.scatter(\n",
    "            y_pred, residuals, c=color, alpha=self.alpha, label=label\n",
    "        )\n",
    "\n",
    "        # Add residuals histogram\n",
    "        if self.hist in {True, 'frequency'}:\n",
    "            self.hax.hist(residuals, bins=50, orientation=\"horizontal\")\n",
    "        elif self.hist == 'density':\n",
    "            self.hax.hist(\n",
    "                residuals, bins=50, orientation=\"horizontal\", density=True\n",
    "            )\n",
    "\n",
    "        # Ensure the current axes is always the main residuals axes\n",
    "        plt.sca(self.ax)\n",
    "        return self.ax\n",
    "\n",
    "    def finalize(self, **kwargs):\n",
    "        \"\"\"\n",
    "        Finalize executes any subclass-specific axes finalization steps.\n",
    "        The user calls show and show calls finalize.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        kwargs: generic keyword arguments.\n",
    "        \"\"\"\n",
    "        # Add the title to the plot\n",
    "        self.set_title('Residuals for {} Model'.format(self.name))\n",
    "\n",
    "        # Set the legend with full opacity patches using manual legend\n",
    "        manual_legend(\n",
    "            self, self._labels, self._colors, loc='best', frameon=True\n",
    "        )\n",
    "\n",
    "        # Create a full line across the figure at zero error.\n",
    "        self.ax.axhline(y=0, c=self.colors['line'])\n",
    "\n",
    "        # Set the axes labels\n",
    "        self.ax.set_ylabel('Residuals')\n",
    "        self.ax.set_xlabel(\"Predicted Value\")\n",
    "\n",
    "        # Finalize the histogram axes\n",
    "        if self.hist:\n",
    "            self.hax.axhline(y=0, c=self.colors['line'])\n",
    "            self.hax.set_xlabel(\"Distribution\")\n",
    "\n",
    "\n",
    "def residuals_plot(model,\n",
    "                   X,\n",
    "                   y,\n",
    "                   ax=None,\n",
    "                   hist=True,\n",
    "                   test_size=0.25,\n",
    "                   train_color='b',\n",
    "                   test_color='g',\n",
    "                   line_color=LINE_COLOR,\n",
    "                   random_state=None,\n",
    "                   alpha=1.0,\n",
    "                   **kwargs):\n",
    "    \"\"\"Quick method:\n",
    "\n",
    "    Divides the dataset X, y into a train and test split (the size of the\n",
    "    splits determined by test_size) then plots the training and test residuals\n",
    "    agains the predicted value for the given model.\n",
    "\n",
    "    This helper function is a quick wrapper to utilize the ResidualsPlot\n",
    "    ScoreVisualizer for one-off analysis.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : a Scikit-Learn regressor\n",
    "        Should be an instance of a regressor, otherwise will raise a\n",
    "        YellowbrickTypeError exception on instantiation.\n",
    "\n",
    "    X  : ndarray or DataFrame of shape n x m\n",
    "        A matrix of n instances with m features.\n",
    "\n",
    "    y  : ndarray or Series of length n\n",
    "        An array or series of target or class values.\n",
    "\n",
    "    ax : matplotlib Axes, default: None\n",
    "        The axes to plot the figure on. If None is passed in the current axes\n",
    "        will be used (or generated if required).\n",
    "\n",
    "    hist : {True, False, None, 'density', 'frequency'}, default: True\n",
    "        Draw a histogram showing the distribution of the residuals on the\n",
    "        right side of the figure. Requires Matplotlib >= 2.0.2.\n",
    "        If set to 'density', the probability density function will be plotted.\n",
    "        If set to True or 'frequency' then the frequency will be plotted.\n",
    "\n",
    "    test_size : float, int default: 0.25\n",
    "        If float, should be between 0.0 and 1.0 and represent the proportion\n",
    "        of the dataset to include in the test split. If int, represents the\n",
    "        absolute number of test samples.\n",
    "\n",
    "    train_color : color, default: 'b'\n",
    "        Residuals for training data are ploted with this color but also\n",
    "        given an opacity of 0.5 to ensure that the test data residuals\n",
    "        are more visible. Can be any matplotlib color.\n",
    "\n",
    "    test_color : color, default: 'g'\n",
    "        Residuals for test data are plotted with this color. In order to\n",
    "        create generalizable models, reserved test data residuals are of\n",
    "        the most analytical interest, so these points are highlighted by\n",
    "        having full opacity. Can be any matplotlib color.\n",
    "\n",
    "    line_color : color, default: dark grey\n",
    "        Defines the color of the zero error line, can be any matplotlib color.\n",
    "\n",
    "    random_state : int, RandomState instance or None, optional\n",
    "        Passed to the train_test_split function.\n",
    "\n",
    "    alpha : float, default: 1.0\n",
    "        Specify a transparency where 1 is completely opaque and 0 is completely\n",
    "        transparent. This property makes densely clustered points more visible.\n",
    "\n",
    "    kwargs : dict\n",
    "        Keyword arguments that are passed to the base class and may influence\n",
    "        the visualization as defined in other Visualizers.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : matplotlib axes\n",
    "        Returns the axes that the residuals plot was drawn on.\n",
    "    \"\"\"\n",
    "    # Instantiate the visualizer\n",
    "    visualizer = ResidualsPlot(\n",
    "        model=model, ax=ax, hist=hist, train_color=train_color,\n",
    "        test_color=test_color, line_color=line_color, alpha=alpha,\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    # Create the train and test splits\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=test_size, random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Fit and transform the visualizer (calls draw)\n",
    "    visualizer.fit(X_train, y_train, **kwargs)\n",
    "    visualizer.score(X_test, y_test)\n",
    "    visualizer.finalize()\n",
    "\n",
    "    # Return the axes object on the visualizer\n",
    "    return visualizer.ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge()\n",
    "visualizer = ResidualsPlot(ridge, alpha=0.9)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the model\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data\n",
    "visualizer.show()                 # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(visualizer.ax.scatter())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
